Probabilistic forecasting of wind power production losses in cold climates: A case study

Abstract

Abstract.The problem of icing on wind turbines in cold climates is addressed using probabilistic forecasting to improve next-day forecasts of icing and related production losses. A case study of probabilistic forecasts was generated for a two-week period.Uncertainties in initial and boundary conditions are represented with an ensemble forecasting system, while uncertainties in the spatial representation are included with a neighbourhood method. Using probabilistic forecasting instead of one single 5 forecast was shown to improve the forecast skill of the ice-related production loss forecasts and hence the icing forecasts.The spread of the multiple forecasts can be used as an estimate of the forecast uncertainty and of the likelihood for icing and severe production losses. Best results, both in terms of forecast skill and forecasted uncertainty, were achieved using both the ensemble forecast and the neighbourhood method combined. This demonstrates that the application of probabilistic forecasting for wind power in cold climate can be valuable when planning next-day energy production, in the usage of de-icing systems, 10 and for site safety.

Sensitivity of Mesoscale Model Forecasts of Icing Conditions to Land Use: Dynamic and Diabatic Impacts

“…Output from each simulation was run through an icing model based on the one described in [6] and adapted to wind turbine use in [17]. This is the same icing model used by [5]. The model is based upon the equation:…”

“…where M is the mass of the accumulated ice in kg, t is time, w is the liquid water content, V is the wind speed, D is the diameter of the cylinder, and α 1 , α 2 and α 3 are the collision, sticking, and accretion efficiencies, respectively. L is an ice loss term that combines a series of functions to take ice shedding, melting, sublimation, and wind erosion into account [5]. Equation 1 uses as input from the NWP output wind speed, temperature, pressure, cloud water, ice, rain, graupel, snow, and specific humidity.…”

“…As a result, ice prediction methods have been developed in the form of short term forecast models [3] and regional ice climate atlases [4]. Although much work has been done in the past decade to develop ice prediction products, this forecast problem continues to be a complex challenge [5]. Icing models, such as that described in [6], have been developed and tested in lab settings, but rely upon prognostic meteorological data input from numerical weather prediction (NWP) models in order to produce predictions of ice load.…”

“…In these regions, the typical icing event occurs due to low level clouds containing supercooled liquid water intersecting terrain, impacting the turbine structure [3]. [5] discusses the impact of errors due to localization that can occur in regions with subgrid complex topography and mitigating these errors with an ensemble method. Land cover representation and the modelling of surface-atmosphere fluxes in areas of heterogeneous land surface cover has been a hotly contested topic for decades, with papers focusing on the sensitivity of the forecast of temperature and wind to changes in surface albedo [7] and roughness [8].…”

Short-range solar radiation forecasts over Sweden

“…Nevertheless, high-resolution NWP can be utilized, especially when the unpredictable components in the forecast can be removed by the use of an ensemble prediction system. This method was demonstrated by using an ensemble of HARMONIE-AROME for wind power in cold climates by Söderman et al (2017). Here, some case studies will show that regional ensemble models can indeed provide added value to forecasts of solar radiation available for PV power production.…”